Genetic programming using dynamic population variation for computational efforts reduction in system modeling

In this paper, we propose genetic programming (GP) using dynamic population variation (DPV) with four innovations for reducing computational efforts. A new stagnation phase definition and characteristic measure are defined for our DPV. The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition. An appropriate population variation formula is suggested to accelerate convergence. The efficacy of these innovations in our DPV is examined using six benchmark problems. Comparison among the different characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures. It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases. Meanwhile, our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.     

Pattern Specialization Based on Partial Evaluation

Design pattern enables software architecture generality and reusability, but which depresses the high performance. The pattern specialization was built on partial evaluation technology to reduce the overheads of design pattern. The design patterns were classified to extract the common features, and the corresponding pattern specializations were constructed. In the pattern specialization, the optimization opportunities were identified, and the specialization methods and conditions were described. The syntax of binding time analysis was defined, and the semantic depicted the invariant of usage context. The virtual invocation and dispatch were eliminated, which enhances the running efficiency. This pattern specialization is a high-level specialization for improving the performance of software aimed at design level that is orthogonal with the low-level code optimization.